Technical Debt, Monoliths, And Microservices: Hexaware's Path To AI Readiness

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Mar 1, 2026

Apurva Kadakia, Global Head of Cloud and Partnerships at Hexaware, leads cloud transformation and AI-ready strategies. He discusses how monolithic architectures and hidden integration debt block AI scale. He explains assessing app portfolios before cloud moves. He outlines using AI to accelerate discovery and choosing the right modernization paths to avoid spiraling costs and governance gaps.

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INSIGHT

Technical Debt Moves To Cloud With Lift And Shift

Many enterprises carry technical debt into the cloud via monolithic, tightly intertwined systems that are hard to maintain.
Apurva Kadakia notes SME shortages and decades-old architectures make future compute and AI strategies costly and risky.

ADVICE

Assess Application Purpose Before Migrating

Do assess each application's purpose before migrating and classify as customer-facing, business-critical, or back-office.
Apurva Kadakia recommends greenfield rebuilds for customer-facing apps and transformation for corporate apps to preserve CX and reduce risk.

ADVICE

Automate Discovery With Human Validation

Use AI-led and human-perfected tools to automate discovery and classification to cover ~60–70% of assessment work.
Hexaware builds internal AI agents to detect patterns then relies on humans to validate outputs for accuracy and timing.

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*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "request-WEB:927f9cca-7aa3-47be-8fb7-33bf01261dc7-6" data-testid= "conversation-turn-14" data-scroll-anchor="true" data-turn= "assistant"> Is your cloud foundation ready for the explosion of AI workloads, or are you about to scale technical debt at the speed of innovation?

In this episode, I'm joined by Apurva Kadakia, Global Head of Cloud and Partnerships at Hexaware, an AI-first transformation company helping enterprises modernize the core systems that will determine whether their AI strategies succeed or stall. With a front-row seat to large-scale cloud programs across industries, Apurva explains why so many organizations that "moved to the cloud" still find themselves unprepared for what comes next, and why modernization-led migration has become a business priority rather than a technology upgrade.

We unpack the real warning signs that cloud environments are not fit for AI, from monolithic architectures and spiraling compute costs to hidden integration complexity and security gaps that only surface at scale. Apurva introduces the idea of "clarity before cloud," a structured approach to understanding sprawling application estates, identifying what truly matters to the business, and matching each workload to the right modernization path using the five R's. It's a conversation that moves beyond theory into the practical decisions leaders need to make now if they want to avoid being locked out of future innovation.

The role of AI inside the transformation journey is another major theme. Rather than treating AI as a destination, Apurva shares how AI-led and human-perfected assessment models are already accelerating application discovery, classification, and migration planning, completing the majority of the heavy lifting while keeping human judgment firmly in control. We also explore why governance cannot be an afterthought, and how a dedicated Cloud Transformation Office can drive adoption, reskilling, stakeholder alignment, and data readiness without slowing delivery.

Looking ahead to a world of agentic systems and rapidly multiplying cloud workloads, this episode offers a clear message. The organizations that win will not be the ones that adopted cloud first, but the ones that modernized with intent.

So as AI moves from experimentation to enterprise scale, are your applications, your architecture, and your operating model truly ready to support it, or is now the moment to rethink your path before the next wave hits?